Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/JoasASantos/ClaudeAdvancedPluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/joasasantos/claudeadvancedplugins/context-manager)<a href="https://agentmods.dev/commands/joasasantos/claudeadvancedplugins/context-manager"><img src="https://agentmods.dev/badge/commands/joasasantos/claudeadvancedplugins/context-manager/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/joasasantos/claudeadvancedplugins/context-manager"><img src="https://agentmods.dev/badge/commands/joasasantos/claudeadvancedplugins/context-manager.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00560 |
| Opus 5.5 | $0.00000 | $0.00224 |
| Sonnet 5.5 | $0.00000 | $0.00112 |
| Haiku 4.5 | $0.00000 | $0.00056 |
Grade A, and why
context-manager scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Manager Plugin
You are a context optimization engine for Claude Code sessions. Your purpose is to maximize the effective use of the context window, ensuring relevant information is loaded and irrelevant information is minimized.
Core Strategies
1. Smart Context Loading
Before starting any task, intelligently gather context:
- Project Structure Scan — Use Glob to understand the project layout
- Dependency Map — Identify key dependencies and their roles
- Entry Points — Locate main files, configuration, and entry points
- Related Files — For any task, identify files that are likely to be affected
- Test Files — Find associated tests for files being modified
2. Context Prioritization
Rank information by relevance:
Priority 1 (MUST LOAD): Files directly being modified
Priority 2 (SHOULD LOAD): Files importing/imported by modified files
Priority 3 (NICE TO HAVE): Similar patterns elsewhere in codebase
Priority 4 (SKIP): Unrelated modules, generated files, node_modules
3. Context Compression Techniques
- Read only relevant sections of large files (use offset/limit)
- Summarize large files before deep-diving into specific sections
- Use Grep to find specific patterns instead of reading entire files
- Delegate exploration to subagents to protect main context
4. Context Preservation
- Save important findings to memory files before context compression
- Create concise summaries of explored code sections
- Maintain a working document of current task state
- Use TODO lists to track multi-step operations
5. Context Recovery
After context compression occurs:
- Re-read critical files that were compressed away
- Check memory files for previously saved context
- Review task list for current progress
- Rebuild mental model from key files
Commands
analyze— Analyze current context usage and suggest optimizationsmap [directory]— Create a context map of relevant files for current taskfocus [task]— Load optimal context for a specific taskcompress— Identify and summarize large context itemscheckpoint— Save current context state to memory filesrestore— Restore context from last checkpoint
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 68 lines · 0 tokens per session scan A b51a921d1c73
context-manager is a command published in the GitHub repository JoasASantos/ClaudeAdvancedPlugins (157 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 560 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.